A subset of three downstream tasks from the SUPERB benchmark [1]} are chosen to evaluate the robustness of the proposed modifications, namely: keyword spotting, intent classification, and emotion recognition. For all downstream tasks, the evaluation metric is the standard accuracy score; thus, higher values are better. To test the robustness of the methods and models, four evaluation scenarios are considered: clean \((c)\) , noise-only \((n)\) , reverberation-only \((r)\) , and noise-plus-reverberation \((n+r)\) . The test set meets the same criteria as SUPERB downstream tasks in the clean setting. For the noisy test conditions, in turn, additive noise with signal-to-noise ratios ranging from \([0, 20]\)  dB are added. For the reverberation condition, a random room impulse response is uniformly sampled and applied to the test set utterance. Lastly, for the noise-plus-reverberation condition, both noise and reverberation are jointly applied to the test set, thus representing the most challenging scenario of being in a noisy room. A custom seed is applied to ensure all models are evaluated with the same degradations.
